mlsanityData to 14 Oct 2026

SIn-NeRF2NeRF: Editing 3D Scenes with Instructions through Segmentation and Inpainting

arXiv 2408.13285 ↗First posted 23 Aug 2024cs.CV cs.AIcited by 1

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Abstract (arXiv)

TL;DR Perform 3D object editing selectively by disentangling it from the background scene. Instruct-NeRF2NeRF (in2n) is a promising method that enables editing of 3D scenes composed of Neural Radiance Field (NeRF) using text prompts. However, it is challenging to perform geometrical modifications such as shrinking, scaling, or moving on both the background and object simultaneously. In this project, we enable geometrical changes of objects within the 3D scene by selectively editing the object after separating it from the scene. We perform object segmentation and background inpainting respectively, and demonstrate various examples of freely resizing or moving disentangled objects within the three-dimensional space.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 6 references that are in the map. First posted on arXiv 23 Aug 2024.

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5 of 6 references.

References · 5

  1. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,723
  2. 2021Resolution-robust Large Mask Inpainting with Fourier Convolutionscited by 738
  3. 2021Depth-supervised NeRF: Fewer Views and Faster Training for Freecited by 735
  4. 2022NeuMan: Neural Human Radiance Field from a Single Videocited by 244
  5. 2023LayerDiffusion: Layered Controlled Image Editing with Diffusion Modelscited by 40

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